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integrate linear and circular processes, enabling used products to be transformed into new generations. What you will do Implement GPU-accelerated Gaussian Mixture Model (GMM) learning in PyTorch Optimize
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suitable for part-time employment. Starting date: 17.10.2025 Job description: Design, develop and apply an flexible and integrative multiscale FWI using GPU-accelerated spectral-element simulations (Salvus
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: metrics, configs, checkpoints, weight versioning, model registry Simulation and Testing: Run large-scale cloud experiments; track throughput, GPU utilization, cost per run; evaluate robustness to preemption
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science, mathematics, statistics, computational linguistics, physics, electrical engineering, or similar with good grades PyTorch skills: experience in training machine learning models with one or more GPUs; ability to
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, superconductivity, cryogenics, or microwave electronics. Additional experience beyond the PhD is not required. US citizenship is not required. What we offer State of the art on-site high performance/GPU compute
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applied Machine Learning Hands-on experience with High Performance Computing Systems Basic knowledge of System Architecture of Supercomputers and NVidia-GPUs Practical experience with ML/DL workflows and
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Gaussian Mixture Model (GMM) learning Contribute to implementation, optimization, and benchmarking tasks in GPU-accelerated environments Assist in preparing experimental results and documentation
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High Performance Computing Systems Basic knowledge of System Architecture of Supercomputers and NVidia-GPUs Practical experience with ML/DL workflows and common software libraries Your experience should
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 29 days ago
we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers to help you balance work and family life
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA